{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import os\n",
    "\n",
    "import torch\n",
    "\n",
    "from scipy.io import loadmat\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device('cuda:0' if use_cuda else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add new methods here.\n",
    "# methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'd2-net', 'd2-net-trained']\n",
    "# names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'D2-Net', 'D2-Net Trained']\n",
    "# colors = ['black', 'orange', 'red', 'red', 'blue', 'purple', 'purple']\n",
    "# linestyles = ['-', '-', '-', '--', '-', '-', '--']\n",
    "methods = ['hesaff', 'hesaffnet', 'delf', 'delf-new', 'superpoint', 'lf-net', 'd2-net', 'd2-net-ms', 'd2-net-trained', 'd2-net-trained-ms']\n",
    "names = ['Hes. Aff. + Root-SIFT', 'HAN + HN++', 'DELF', 'DELF New', 'SuperPoint', 'LF-Net', 'D2-Net', 'D2-Net MS', 'D2-Net Trained', 'D2-Net Trained MS']\n",
    "colors = ['black', 'orange', 'red', 'red', 'blue', 'brown', 'purple', 'green', 'purple', 'green']\n",
    "linestyles = ['-', '-', '-', '--', '-', '-', '-', '-', '--', '--']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change here if you want to use top K or all features.\n",
    "# top_k = 2000\n",
    "top_k = None "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_i = 52\n",
    "n_v = 56"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = 'hpatches-sequences-release'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lim = [1, 15]\n",
    "rng = np.arange(lim[0], lim[1] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mnn_matcher(descriptors_a, descriptors_b):\n",
    "    device = descriptors_a.device\n",
    "    sim = descriptors_a @ descriptors_b.t()\n",
    "    nn12 = torch.max(sim, dim=1)[1]\n",
    "    nn21 = torch.max(sim, dim=0)[1]\n",
    "    ids1 = torch.arange(0, sim.shape[0], device=device)\n",
    "    mask = (ids1 == nn21[nn12])\n",
    "    matches = torch.stack([ids1[mask], nn12[mask]])\n",
    "    return matches.t().data.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def benchmark_features(read_feats):\n",
    "    seq_names = sorted(os.listdir(dataset_path))\n",
    "\n",
    "    n_feats = []\n",
    "    n_matches = []\n",
    "    seq_type = []\n",
    "    i_err = {thr: 0 for thr in rng}\n",
    "    v_err = {thr: 0 for thr in rng}\n",
    "\n",
    "    for seq_idx, seq_name in tqdm(enumerate(seq_names), total=len(seq_names)):\n",
    "        keypoints_a, descriptors_a = read_feats(seq_name, 1)\n",
    "        n_feats.append(keypoints_a.shape[0])\n",
    "\n",
    "        for im_idx in range(2, 7):\n",
    "            keypoints_b, descriptors_b = read_feats(seq_name, im_idx)\n",
    "            n_feats.append(keypoints_b.shape[0])\n",
    "\n",
    "            matches = mnn_matcher(\n",
    "                torch.from_numpy(descriptors_a).to(device=device), \n",
    "                torch.from_numpy(descriptors_b).to(device=device)\n",
    "            )\n",
    "            \n",
    "            homography = np.loadtxt(os.path.join(dataset_path, seq_name, \"H_1_\" + str(im_idx)))\n",
    "            \n",
    "            pos_a = keypoints_a[matches[:, 0], : 2] \n",
    "            pos_a_h = np.concatenate([pos_a, np.ones([matches.shape[0], 1])], axis=1)\n",
    "            pos_b_proj_h = np.transpose(np.dot(homography, np.transpose(pos_a_h)))\n",
    "            pos_b_proj = pos_b_proj_h[:, : 2] / pos_b_proj_h[:, 2 :]\n",
    "\n",
    "            pos_b = keypoints_b[matches[:, 1], : 2]\n",
    "\n",
    "            dist = np.sqrt(np.sum((pos_b - pos_b_proj) ** 2, axis=1))\n",
    "\n",
    "            n_matches.append(matches.shape[0])\n",
    "            seq_type.append(seq_name[0])\n",
    "            \n",
    "            if dist.shape[0] == 0:\n",
    "                dist = np.array([float(\"inf\")])\n",
    "            \n",
    "            for thr in rng:\n",
    "                if seq_name[0] == 'i':\n",
    "                    i_err[thr] += np.mean(dist <= thr)\n",
    "                else:\n",
    "                    v_err[thr] += np.mean(dist <= thr)\n",
    "    \n",
    "    seq_type = np.array(seq_type)\n",
    "    n_feats = np.array(n_feats)\n",
    "    n_matches = np.array(n_matches)\n",
    "    \n",
    "    return i_err, v_err, [seq_type, n_feats, n_matches]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def summary(stats):\n",
    "    seq_type, n_feats, n_matches = stats\n",
    "    print('# Features: {:f} - [{:d}, {:d}]'.format(np.mean(n_feats), np.min(n_feats), np.max(n_feats)))\n",
    "    print('# Matches: Overall {:f}, Illumination {:f}, Viewpoint {:f}'.format(\n",
    "        np.sum(n_matches) / ((n_i + n_v) * 5), \n",
    "        np.sum(n_matches[seq_type == 'i']) / (n_i * 5), \n",
    "        np.sum(n_matches[seq_type == 'v']) / (n_v * 5))\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_read_function(method, extension='ppm'):\n",
    "    def read_function(seq_name, im_idx):\n",
    "        aux = np.load(os.path.join(dataset_path, seq_name, '%d.%s.%s' % (im_idx, extension, method)))\n",
    "        if top_k is None:\n",
    "            return aux['keypoints'], aux['descriptors']\n",
    "        else:\n",
    "            assert('scores' in aux)\n",
    "            ids = np.argsort(aux['scores'])[-top_k :]\n",
    "            return aux['keypoints'][ids, :], aux['descriptors'][ids, :]\n",
    "    return read_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sift_to_rootsift(descriptors):\n",
    "    return np.sqrt(descriptors / np.expand_dims(np.sum(np.abs(descriptors), axis=1), axis=1) + 1e-16)\n",
    "def parse_mat(mat):\n",
    "    keypoints = mat['keypoints'][:, : 2]\n",
    "    raw_descriptors = mat['descriptors']\n",
    "    l2_norm_descriptors = raw_descriptors / np.expand_dims(np.sum(raw_descriptors ** 2, axis=1), axis=1)\n",
    "    descriptors = sift_to_rootsift(l2_norm_descriptors)\n",
    "    if top_k is None:\n",
    "        return keypoints, descriptors\n",
    "    else:\n",
    "        assert('scores' in mat)\n",
    "        ids = np.argsort(mat['scores'][0])[-top_k :]\n",
    "        return keypoints[ids, :], descriptors[ids, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if top_k is None:\n",
    "    cache_dir = 'cache'\n",
    "else:\n",
    "    cache_dir = 'cache-top'\n",
    "if not os.path.isdir(cache_dir):\n",
    "    os.mkdir(cache_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "errors = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hesaff\n",
      "Loading precomputed errors...\n",
      "# Features: 6710.137346 - [296, 26021]\n",
      "# Matches: Overall 2851.679630, Illumination 1585.803846, Viewpoint 4027.135714\n",
      "hesaffnet\n",
      "Loading precomputed errors...\n",
      "# Features: 3860.754630 - [89, 16326]\n",
      "# Matches: Overall 1959.996296, Illumination 1098.419231, Viewpoint 2760.032143\n",
      "delf\n",
      "Loading precomputed errors...\n",
      "# Features: 4608.236111 - [1196, 10939]\n",
      "# Matches: Overall 1912.400000, Illumination 1973.100000, Viewpoint 1856.035714\n",
      "delf-new\n",
      "Loading precomputed errors...\n",
      "# Features: 4590.001543 - [953, 12696]\n",
      "# Matches: Overall 1940.288889, Illumination 2031.873077, Viewpoint 1855.246429\n",
      "superpoint\n",
      "Loading precomputed errors...\n",
      "# Features: 1562.611111 - [90, 6422]\n",
      "# Matches: Overall 883.440741, Illumination 667.830769, Viewpoint 1083.650000\n",
      "lf-net\n",
      "Loading precomputed errors...\n",
      "# Features: 500.000000 - [500, 500]\n",
      "# Matches: Overall 177.475926, Illumination 183.073077, Viewpoint 172.278571\n",
      "d2-net\n",
      "Loading precomputed errors...\n",
      "# Features: 2994.067901 - [641, 9337]\n",
      "# Matches: Overall 1182.574074, Illumination 964.588462, Viewpoint 1384.989286\n",
      "d2-net-ms\n",
      "Loading precomputed errors...\n",
      "# Features: 4928.163580 - [1009, 15230]\n",
      "# Matches: Overall 1698.377778, Illumination 1384.215385, Viewpoint 1990.100000\n",
      "d2-net-trained\n",
      "Loading precomputed errors...\n",
      "# Features: 5965.117284 - [1309, 18974]\n",
      "# Matches: Overall 2495.900000, Illumination 2033.250000, Viewpoint 2925.503571\n",
      "d2-net-trained-ms\n",
      "Loading precomputed errors...\n",
      "# Features: 8254.473765 - [1797, 26880]\n",
      "# Matches: Overall 2831.638889, Illumination 2313.957692, Viewpoint 3312.342857\n"
     ]
    }
   ],
   "source": [
    "for method in methods:\n",
    "    output_file = os.path.join(cache_dir, method + '.npy')\n",
    "    print(method)\n",
    "    if method == 'hesaff':\n",
    "        read_function = lambda seq_name, im_idx: parse_mat(loadmat(os.path.join(dataset_path, seq_name, '%d.ppm.hesaff' % im_idx), appendmat=False))\n",
    "    else:\n",
    "        if method == 'delf' or method == 'delf-new':\n",
    "            read_function = generate_read_function(method, extension='png')\n",
    "        else:\n",
    "            read_function = generate_read_function(method)\n",
    "    if os.path.exists(output_file):\n",
    "        print('Loading precomputed errors...')\n",
    "        errors[method] = np.load(output_file, allow_pickle=True)\n",
    "    else:\n",
    "        errors[method] = benchmark_features(read_function)\n",
    "        np.save(output_file, errors[method])\n",
    "    summary(errors[method][-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_lim = [1, 10]\n",
    "plt_rng = np.arange(plt_lim[0], plt_lim[1] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('axes', titlesize=25)\n",
    "plt.rc('axes', labelsize=25)\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "plt.subplot(1, 3, 1)\n",
    "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    plt.plot(plt_rng, [(i_err[thr] + v_err[thr]) / ((n_i + n_v) * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
    "plt.title('Overall')\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "plt.ylabel('MMA')\n",
    "plt.ylim([0, 1])\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    plt.plot(plt_rng, [i_err[thr] / (n_i * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
    "plt.title('Illumination')\n",
    "plt.xlabel('threshold [px]')\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "plt.ylim([0, 1])\n",
    "plt.gca().axes.set_yticklabels([])\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "for method, name, color, ls in zip(methods, names, colors, linestyles):\n",
    "    i_err, v_err, _ = errors[method]\n",
    "    plt.plot(plt_rng, [v_err[thr] / (n_v * 5) for thr in plt_rng], color=color, ls=ls, linewidth=3, label=name)\n",
    "plt.title('Viewpoint')\n",
    "plt.xlim(plt_lim)\n",
    "plt.xticks(plt_rng)\n",
    "plt.ylim([0, 1])\n",
    "plt.gca().axes.set_yticklabels([])\n",
    "plt.grid()\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "\n",
    "if top_k is None:\n",
    "    plt.savefig('hseq.pdf', bbox_inches='tight', dpi=300)\n",
    "else:\n",
    "    plt.savefig('hseq-top.pdf', bbox_inches='tight', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
